Submission¶

Put the ipynb file and html file in the github branch you created in the last assignment and submit the link to the commit in brightspace

In [1]:
from plotly.offline import init_notebook_mode
import plotly.io as pio
import plotly.express as px

init_notebook_mode(connected=True)
pio.renderers.default = "plotly_mimetype+notebook"
In [2]:
#load data
df = px.data.gapminder()
df.head()
Out[2]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4

Question 1:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [3]:
# YOUR CODE HERE

df_2007 = df.query("year==2007")

# Group by continent and sum the population
df_2007_grouped = df_2007.groupby('continent', as_index=False).agg({'pop': 'sum'})

# Sort by population
df_2007_grouped = df_2007_grouped.sort_values(by='pop', ascending=False)
        

fig = px.bar(df_2007_grouped, y='continent', x='pop', color='continent', text='pop')
fig.update_layout(
    title='Population of Different Continents in 2007',
    xaxis_title='Continent',
    yaxis_title='Population',
    xaxis=dict(categoryorder='total descending'),
)
fig.show()

Question 2:¶

Sort the order of the continent for the visualisation

Hint: Use axis layout setting

In [4]:
# YOUR CODE HERE

# The requirements for Q2 and Q3 are all completed in the Q1 code, so codes in Q2 and Q3 are the same with the codes in Q1.

fig = px.bar(df_2007_grouped, y='continent', x='pop', color='continent', text='pop')
fig.update_layout(
    title='Population of Different Continents in 2007',
    xaxis_title='Continent',
    yaxis_title='Population',
    xaxis=dict(categoryorder='total descending'),
)
fig.show()

Question 3:¶

Add text to each bar that represents the population

In [5]:
# YOUR CODE HERE

# The requirements for Q2 and Q3 are all completed in the Q1 code, so codes in Q2 and Q3 are the same with the codes in Q1.

fig = px.bar(df_2007_grouped, y='continent', x='pop', color='continent', text='pop')
fig.update_layout(
    title='Population of Different Continents in 2007',
    xaxis_title='Continent',
    yaxis_title='Population',
    xaxis=dict(categoryorder='total descending'),
)
fig.show()

Question 4:¶

Thus far we looked at data from one year (2007). Lets create an animation to see the population growth of the continents through the years

In [11]:
# YOUR CODE HERE

df_grouped = df.groupby(['year', 'continent'], as_index=False).agg({'pop': 'sum'})

fig = px.bar(df_grouped,
             y='continent', 
             x='pop', 
             color='continent', 
             animation_frame='year',
             animation_group='continent',
             title='Population Growth of Continents Over the Years',
             labels={'pop': 'Population', 'continent': 'Continent'},
             text='pop',
             orientation='h')

fig.update_layout(
    xaxis_title='Continent',
    yaxis_title='Population',
    xaxis=dict(range=[0, df_grouped['pop'].max()]),
    yaxis=dict(categoryorder='total ascending'),
    showlegend=False
)

fig.show()

Question 5:¶

Instead of the continents, lets look at individual countries. Create an animation that shows the population growth of the countries through the years

In [16]:
# YOUR CODE HERE

df_grouped = df.groupby(['year', 'country'], as_index=False).agg({'pop': 'sum'})

fig = px.bar(df_grouped,
             y='country', 
             x='pop', 
             color='country', 
             animation_frame='year',
             animation_group='country',
             title='Population Growth of Countries Over the Years',
             labels={'pop': 'Population', 'country': 'Country'},
             # text='pop'     # delete text because texts are too small to work in normal animation.
            )

fig.update_layout(
    xaxis_title='Continent',
    yaxis_title='Population',
    xaxis=dict(range=[0, df_grouped['pop'].max()]),
    yaxis=dict(categoryorder='total ascending'),
    showlegend=False
)

fig.show()

Question 6:¶

Clean up the country animation. Set the height size of the figure to 1000 to have a better view of the animation

In [18]:
# YOUR CODE HERE

df_grouped = df.groupby(['year', 'country'], as_index=False).agg({'pop': 'sum'})

fig = px.bar(df_grouped,
             y='country', 
             x='pop', 
             color='country', 
             animation_frame='year',
             animation_group='country',
             title='Population Growth of Countries Over the Years',
             labels={'pop': 'Population', 'country': 'Country'},
             # text='pop'     # delete text because texts are too small to work in normal animation.
            )

fig.update_layout(
    xaxis_title='Country',
    yaxis_title='Population',
    xaxis=dict(range=[0, df_grouped['pop'].max()]),
    yaxis=dict(categoryorder='total ascending'),
    showlegend=False,
    height=1000   # Set the height size of the figure to 1000 to have a better view of the animation
)

fig.show()

Question 7:¶

Show only the top 10 countries in the animation

Hint: Use the axis limit to set this.

In [20]:
# YOUR CODE HERE

# Group the top 10 countries
df_grouped['rank'] = df_grouped.groupby('year')['pop'].rank(method='first', ascending=False)
df_top10 = df_grouped[df_grouped['rank'] <= 10]

fig = px.bar(df_top10,
             y='country', 
             x='pop', 
             color='country', 
             animation_frame='year',
             animation_group='country',
             title='Top 10 Most Populated Countries Over the Years',
             labels={'pop': 'Population', 'country': 'Country'},
             text='pop'
            )

fig.update_layout(
    xaxis_title='Country',
    yaxis_title='Population',
    xaxis=dict(range=[0, df_top10['pop'].max()]),
    yaxis=dict(categoryorder='total ascending'),
    showlegend=False,
    height=1000
    )
fig.show()